Risk and Risk Management in the Credit Card Industry
Using account level credit-card data from six major commercial banks from January 2009 to December 2013, we apply machine-learning techniques to combined consumer-tradeline, credit-bureau, and macroeconomic variables to predict delinquency. In addition to providing accurate measures of loss probabilities and credit risk, our models can also be used to analyze and compare risk management practices and the drivers of delinquency across the banks. We find substantial heterogeneity in risk factors, sensitivities, and predictability of delinquency across banks, implying that no single model applies to all six institutions. We measure the efficacy of a bank’s risk-management process by the percentage of delinquent accounts that a bank manages effectively, and find that efficacy also varies widely across institutions. These results suggest the need for a more customized approached to the supervision and regulation of financial institutions, in which capital ratios, loss reserves, and other parameters are specified individually for each institution according to its credit-risk model exposures and forecasts.
We thank Michael Carhill, Jayna Cummings, Misha Dobrolioubov , Dennis Glennon, Amir Khandani, Adlar Kim, Mark Levonian, David Nebhut, Til Schuerman, Michael Sullivan and seminar participants at the Consortium for Systemic Risk Analysis, the Consumer Finance Protection Bureau, the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), the Office of the Comptroller of the Currency, and the Philadelphia Fed’s Risk Quantification Forum for useful comments and discussion. The views and opinions expressed in this article are those of the authors only, and do not necessarily represent the views and opinions of any institution or agency, any of their affiliates or employees, or any of the individuals acknowledged above. Research support from the MIT CSAIL Big Data program, the MIT Laboratory for Financial Engineering, and the Office of the Comptroller of the Currency is gratefully acknowledged. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
Andrew W. Lo
Research support from the MIT Laboratory for Financial Engineering and its sponsors is gratefully acknowledged. Sponsors include: The Clearing House, Natixis Global Asset Management, and the Alfred P. Sloan Foundation.
Florentin Butaru & Qingqing Chen & Brian Clark & Sanmay Das & Andrew W. Lo & Akhtar Siddique, 2016. "Risk and risk management in the credit card industry," Journal of Banking & Finance, vol 72, pages 218-239.